#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')

library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
suppressWarnings(suppressMessages(library(WGCNA)))
library(expss)
library(polycor)
library(foreach) ; library(doParallel)

SFARI_colour_hue = function(r) {
  pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}

Load preprocessed dataset (preprocessing code in 20_02_28_data_preprocessing.Rmd) and clustering (pipeline in 20_02_28_WGCNA.Rmd)

# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame


# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>% 
              mutate('ID'=as.character(ensembl_gene_id)) %>% 
              dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
              mutate('Neuronal'=1)


# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]


# Clusterings
clusterings = read_csv('./../Data/clusters.csv')


# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
  mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
  left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
  mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
  mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`), 
         significant=padj<0.05 & !is.na(padj))


rm(DE_info, GO_annotations, clusterings)

Dynamic Tree vs Dyamic Hybrid

print(paste0('Dynamic Tree leaves ', sum(genes_info$DynamicTree=='gray'), ' genes without cluster (', 
             round(mean(genes_info$DynamicTree=='gray')*100), '%)'))

## [1] "Dynamic Tree leaves 531 genes without cluster (3%)"

print(paste0('Dynamic Hybrid leaves ', sum(genes_info$DynamicHybrid=='gray'), ' genes without cluster (', 
             round(mean(genes_info$DynamicHybrid=='gray')*100,2), '%)'))

## [1] "Dynamic Hybrid leaves 223 genes without cluster (1.38%)"

Dynamic Tree leaves more genes without a cluster, but in previous experiments it returned cleaner results, so I’m going to see which genes are lost to see how big the damage is.

When studying all the brian regions together, there seemed to be a relation between DE and module membership, being DE a more restrictive condition than being assigned to a cluster. Here it’s not that easy to distinguish given the small amount of DE genes and genes left without a cluster, but they could still be related since genes without a cluser have a very small PC2 and DE genes very high absolute PC2

pca = datExpr %>% prcomp

plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
            left_join(genes_info, by='ID') %>% mutate('hasCluster'=DynamicTree!='gray', 
                                                      'hasSFARIScore'=`gene-score`!='None') %>%
            apply_labels(`gene-score`='SFARI Gene score', DynamicTree = 'Dynamic Tree Algorithm', 
                         significant = 'Differentially Expressed', hasCluster = 'Belongs to a Module',
                         hasSFARIScore = 'Has a SFARI Score', syndromic = 'Has syndromic tag')

p1 = plot_data %>% ggplot(aes(PC1, PC2, color=hasCluster)) + geom_point(alpha=1-0.9*plot_data$hasCluster) + 
  theme_minimal() + ggtitle('Genes are assigned to a cluster') + theme(legend.position='bottom')

p2 = plot_data %>% ggplot(aes(PC1, PC2, color=significant)) + geom_point(alpha=plot_data$significant/2+0.1) + 
  theme_minimal() + ggtitle('Genes found to be DE') + theme(legend.position='bottom')

grid.arrange(p1, p2, nrow=1)

rm(pca, p1, p2)

Most of the genes that don’t have a cluster are not differentially expressed, but almost all genes are not differentially expressed, so this isn’t that remarkable

cat(paste0(round(100*sum(!plot_data$hasCluster & !plot_data$significant)/sum(!plot_data$hasCluster)),
           '% of the genes that don\'t have a cluster are not differentially expressed\n'))

## 99% of the genes that don't have a cluster are not differentially expressed

cro(plot_data$significant, list(plot_data$hasCluster, total()))

	Belongs to a Module		#Total
	FALSE	TRUE
Differentially Expressed
FALSE	528	15601	16129
TRUE	3	21	24
#Total cases	531	15622	16153

Most of the genes with a SFARI score are assigned to a cluster

cat(paste0(sum(plot_data$hasSFARIScore & !plot_data$hasCluster), ' of the SFARI genes (~',
           round(100*sum(plot_data$hasSFARIScore & !plot_data$hasCluster)/sum(plot_data$hasSFARIScore)),
           '%) are not assigned to any cluster\n'))

## 24 of the SFARI genes (~3%) are not assigned to any cluster

cro(plot_data$hasSFARIScore, list(plot_data$hasCluster, total()))

	Belongs to a Module		#Total
	FALSE	TRUE
Has a SFARI Score
FALSE	507	14746	15253
TRUE	24	876	900
#Total cases	531	15622	16153

Conclusion:

The main ndifference between algorithms is that Dynamic Hybrid clusters outlier genes and Dynamic Tree leaves them out, so Dynamic Tree would give me a ‘cleaner’ group of genes to work with, without losing many SFARI genes, but Dynamic Hybrid has less and more balanced clusters

I think both options could be feasible, but I’m going to use the Dynamic Hybrid algorithm to keep more genes

Since Dynamic Hybrid returned so many modules, I’m going to use the smallest of the merged modules, DynamicHybridMergedSmall

clustering_selected = 'DynamicHybridMergedSmall'
genes_info$Module = genes_info[,clustering_selected]

Dynamic Hybrid Modules

*The colour of the modules is the arbitrary one assigned during the WGCNA algorithm, where the gray cluster actually represents all the genes that were left without a cluster (so it’s not actually a cluster).

cat(paste0('The Dynamic Hybrid algorithm created ', length(unique(genes_info$Module))-1, ' modules and leaves ',
           sum(genes_info$Module=='gray'), ' genes without a module.\n'))

## The Dynamic Hybrid algorithm created 124 modules and leaves 223 genes without a module.

table(genes_info$Module)

## 
## #00A6FF #00A8FF #00AAFD #00ADFB #00AFF8 #00B1F5 #00B3F2 #00B4EF #00B6EB 
##      45     154     193      20      57      27     173     173     142 
## #00B70D #00B7E8 #00B825 #00B934 #00B9E4 #00BA40 #00BAE0 #00BB4A #00BBDB 
##     177      26     224     331     344     111     109      13     118 
## #00BC53 #00BCD7 #00BD5C #00BD64 #00BDD3 #00BE6B #00BE72 #00BEC9 #00BECE 
##      61     207      53      62      16     170      38      32      14 
## #00BF79 #00BF80 #00BFC4 #00C086 #00C08D #00C093 #00C099 #00C0B0 #00C0B5 
##     108     108      66      62      52     181     100      29     304 
## #00C0BA #00C0BF #00C19F #00C1A5 #00C1AA #1EA3FF #29B600 #3DB500 #40A1FF 
##      55      19      15     100      16      58      42      50      27 
## #4CB400 #569EFF #58B300 #62B200 #669BFF #6BB100 #7498FF #74B000 #7CAE00 
##      13     588     466      33     135     369      43      21     714 
## #8195FF #83AD00 #8AAC00 #8C92FF #90AA00 #96A900 #978FFF #9CA700 #A08CFF 
##      70    1003      32      65     226      14      38    1199      44 
## #A2A600 #A7A400 #A989FF #ACA300 #B185FF #B1A100 #B69F00 #B982FF #BA9E00 
##      81     166     212      18     108      54      91      71      45 
## #BF9C00 #C07FFF #C39A00 #C77CFF #C79800 #CB9700 #CD79FF #CF9500 #D29300 
##      11     198      24      47      23      41      63      96      57 
## #D376FF #D69100 #D873FC #D98F00 #DC8D00 #DD71FA #DF8B00 #E26EF7 #E28900 
##      15     891      23      73     160      20      14     288      23 
## #E58709 #E66CF4 #E88524 #EA6AF0 #EB8334 #ED8140 #EE68ED #F07F4B #F166E9 
##      99      99      25     614     108      58      84      38      43 
## #F27C54 #F464E5 #F47A5D #F67865 #F763E1 #F8766D #F962DC #FA7474 #FB61D8 
##     155      47      28      21      29      14      46     541      63 
## #FB727C #FD61D3 #FD7083 #FE61CE #FE6E89 #FF61C4 #FF61C9 #FF62BA #FF62BF 
##     122      34      36      12     117      49     234      11     304 
## #FF63B4 #FF65AE #FF66A9 #FF67A3 #FF699D #FF6B96 #FF6C90    gray 
##      46      31      22     124      39     153      16     223

plot_data = table(genes_info$Module) %>% data.frame %>% arrange(desc(Freq))

ggplotly(plot_data %>% ggplot(aes(x=reorder(Var1, -Freq), y=Freq)) + geom_bar(stat='identity', fill=plot_data$Var1) + 
         ggtitle('Module size') + ylab('Number of genes') + xlab('Module') + theme_minimal() + 
         theme(axis.text.x = element_text(angle = 90)))

Relation to external clinical traits

Quantifying module-trait associations

In the WGCNA documentation they use Pearson correlation to calculate correlations, I think all of their variables were continuous. Since I have categorical variables I’m going to use the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.

I’m not sure how the corPvalueStudent function calculates the p-values and I cannot find any documentation…
Compared correlations using Pearson correlation and with hetcor and they are very similar, but a bit more extreme with hetcor. The same thing happens with the p-values.

datTraits = datMeta %>% dplyr::select(Diagnosis, Sex, Age, PMI, RNAExtractionBatch) %>%
            dplyr::rename('ExtractionBatch' = RNAExtractionBatch)

# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)

# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))

# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)

# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
  print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated')) 
}

## [1] "2 correlation(s) could not be calculated"

rm(ME_object)

Note: The correlations between a Modules and Diagonsis that cannot be calculated, weirdly enough, is because the initial correlation is too high, so it would be a very bad thing to lose these modules because of this numerical error. I’m going to fill in the values using the polyserial function, which doesn’t give exactly the same results as the hetcor() function, but it’s quite similar.

# Calculate the correlation tha failed with hetcor()
missing_modules = rownames(moduleTraitCor)[is.na(moduleTraitCor[,1])]

for(m in missing_modules){
  moduleTraitCor[m,'Diagnosis'] = polyserial(MEs[,m], datTraits$Diagnosis)  
}

## Warning in polyserial(MEs[, m], datTraits$Diagnosis): initial correlation
## inadmissible, 1.05417437498074, set to 0.9999

rm(missing_modules)

Modules have very strong correlations with Diagnosis with really small p-values and not much relation with anything else. Perhaps a little with PMI and Brain Region.

They gray ‘module’ no longer has one of the lowest correlations with Diagnosis

# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]

# Filter some modules so the heatmap is easier to plot
# moduleTraitCor = moduleTraitCor[abs(moduleTraitCor[,1])>0.3,]
# moduleTraitPvalue = moduleTraitPvalue[abs(moduleTraitCor[,1])>0.3,]

# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)


labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels =  gsub('ME','',rownames(moduleTraitCor)), 
               yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
               textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
               main = paste('Module-Trait relationships'))

diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
                           'MTcor' = moduleTraitCor[,'Diagnosis'],
                           'MTpval' = moduleTraitPvalue[,'Diagnosis'])

genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')

rm(moduleTraitCor, moduleTraitPvalue, textMatrix, diagnosis_cor)

It’s harder to see with so little DE genes, but this plot still shows that Modules with a high Module-Diagnosis (absolute) correlation have a higher content of differentially expressed genes

plot_data = genes_info %>% group_by(Module, MTcor) %>% summarise(p = 100*mean(significant))

plot_data %>% ggplot(aes(MTcor, p)) + geom_hline(yintercept=mean(plot_data$p), color='gray', linetype='dotted') +
         geom_point(color=plot_data$Module, aes(id=Module)) + theme_minimal() + 
         xlab('Modules ordered by Module-Diagnosis correlation') + ylab('Percentage of differentially expressed genes')

Gene Significance and Module Membership

Gene significance: is the value between the correlation between the gene and the trait we are interested in. A positive gene significance means the gene is overexpressed and a negative value means its underexpressed. (The term ‘significance’ is not very acurate because it’s not actually measuring statistical significance, it’s just a correlation, but that’s how they call it in WGCNA…)

Module Membership is the correlation of the module’s eigengene and the expression profile of a gene. The higher the Module Membership, the more similar the gene is to the genes that constitute the module. (I won’t use this measure yet)

# It's more efficient to iterate the correlations one by one, otherwise it calculates correlations between the eigengenes and also between the genes, which we don't need

# Check if MM information already exists and if not, calculate it
if(file.exists(paste0('./../Data/dataset_', clustering_selected, '.csv'))){
  
  dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
  dataset$Module = dataset[,clustering_selected]
  
} else {
  
  ############# 1. Calculate Gene Significance
  GS_info = data.frame('ID' = rownames(datExpr),
                       'GS' = datExpr %>% apply(1, function(x) hetcor(x, datMeta$Diagnosis)$correlations[1,2])) %>%
            mutate('GSpval' = corPvalueStudent(GS, ncol(datExpr)))
  
  #############  2. Calculate Module Membership
  
  #setup parallel backend to use many processors
  cores = detectCores()
  cl = makeCluster(cores-1)
  registerDoParallel(cl)
  
  # Create matrix with MM by gene
  MM = foreach(i=1:nrow(datExpr), .combine=rbind) %dopar% {
    library(polycor)
    tempMatrix = apply(MEs, 2, function(x) hetcor(as.numeric(datExpr[i,]), x)$correlations[1,2])
    tempMatrix
  }
  
  # Stop clusters
  stopCluster(cl)
  
  rownames(MM) = rownames(datExpr)
  colnames(MM) = paste0('MM',gsub('ME','',colnames(MEs)))
  
  # Calculate p-values
  MMpval = MM %>% corPvalueStudent(ncol(datExpr)) %>% as.data.frame
  colnames(MMpval) = paste0('MMpval', gsub('ME','',colnames(MEs)))
  
  MM = MM %>% as.data.frame %>% mutate(ID = rownames(.))
  MMpval = MMpval %>% as.data.frame %>% mutate(ID = rownames(.))
  
  # Join and save results
  dataset = genes_info %>% dplyr::select(ID, `gene-score`, clustering_selected, MTcor, MTpval) %>%
            left_join(GS_info, by='ID') %>%
            left_join(MM, by='ID') %>%
            left_join(MMpval, by='ID')
  
  write.csv(dataset, file = paste0('./../Data/dataset_', clustering_selected, '.csv'), row.names = FALSE)
  
  rm(cores, cl) 
  
}

GS_missing = dataset$ID[is.na(dataset$GS)] %>% as.character

if(length(GS_missing)>0){
  
  print(paste0(length(GS_missing),' correlations between genes and Diagnosis could not be calculated, ',
               'calculating them with the polyserial function'))
  
  for(g in GS_missing){
    dataset$GS[dataset$ID == g] = polyserial(as.numeric(datExpr[g,]), datMeta$Diagnosis)
  }
  
}

## [1] "36 correlations between genes and Diagnosis could not be calculated, calculating them with the polyserial function"

rm(GS_missing)

Analysing concordance between these metrics in the genes

1. Gene Significance vs Log Fold Change

Gene significance and Log Fold Chance are two different ways to measure the same thing, so there should be a concordance between them

Log Fold Change seems to have a wider range of values and Gene Significance values seem more uniformly distributed, but they do agree with each other

plot_data = dataset %>% dplyr::select(ID, MTcor, GS) %>% left_join(genes_info %>% dplyr::select(ID, gene.score), by='ID') %>%
            left_join(genes_info %>% dplyr::select(ID, baseMean, log2FoldChange, significant, Module), by='ID') %>%
            left_join(data.frame(MTcor=unique(dataset$MTcor)) %>% arrange(by=MTcor) %>% 
                                 mutate(order=1:length(unique(dataset$MTcor))), by='MTcor')

ggplotly(plot_data %>% ggplot(aes(GS, log2FoldChange)) + geom_point(color=plot_data$Module, alpha=0.5, aes(ID=Module)) + 
         geom_smooth(color='gray') + theme_minimal() + xlab('Gene Significance') + 
         ggtitle(paste0('Correlation = ', round(cor(plot_data$log2FoldChange, plot_data$GS)[1], 4))))

2. Module-Diagnosis correlation vs Gene Significance

In general, modules with the highest Module-Diagnosis correlation should have genes with high Gene Significance

Note: For the Module-Diagnosis plots, if you do boxplots, you lose the exact module-diagnosis correlation and you only keep the order, so I decided to compensate this downside with a second plot, where each point is plotted individually using their module’s Module-Diagnosis correlation as the x axis. I think the boxplot plot is easier to understand but the second plot contains more information, so I don’t know which one is better.

plot_data = plot_data %>% arrange(order)

ggplotly(plot_data %>% ggplot(aes(order, GS, group=order)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
         geom_boxplot(fill=unique(plot_data$Module)) + theme_minimal() + 
         xlab('Modules ordered by Module-Diagnosis correlation') + ylab('Gene Significance'))

plot_data %>% ggplot(aes(MTcor, GS)) + geom_hline(yintercept=0, color='gray', linetype='dotted') + 
         geom_point(color=plot_data$Module, alpha=0.1, aes(id=ID)) + geom_smooth(color='gray', alpha=0.3) + 
         theme_minimal() + xlab('Module-Diagnosis correlation') + ylab('Gene Significance') + 
         ggtitle(paste0('R^2=',round(cor(plot_data$MTcor, plot_data$GS)^2,4)))

3. Module-Diagnosis correlation vs Log Fold Change

The same should happen with the Log Fold Change

ggplotly(plot_data %>% ggplot(aes(order, log2FoldChange, group=order)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
         geom_boxplot(fill=unique(plot_data$Module)) + 
         theme_minimal() + xlab('Modules ordered by Module-Diagnosis correlation') + ylab('log2FoldChange'))

ggplotly(plot_data %>% ggplot(aes(MTcor, log2FoldChange)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
         geom_point(color=plot_data$Module, alpha=0.1, aes(id=ID)) + geom_smooth(color='gray', alpha=0.3) + 
         theme_minimal() + xlab('Module-Diagnosis correlation') + ylab('log2FoldChange') + 
         ggtitle(paste0('R^2=',round(cor(plot_data$MTcor, plot_data$log2FoldChange)^2,4))))

4. Module-Diagnosis vs Mean Expression

When studying this plot using samples from all brain regions, we can see a small delation between module-Diagnosis and mean expression that we could explain by what we had observed where overexpressed genes tended to have lower levels of expression than the overexpressed genes, but this patterns is no longer recognisable on this plot.

ggplotly(plot_data %>% ggplot(aes(order, log2(baseMean+1), group=order)) + 
         geom_hline(yintercept=mean(log2(plot_data$baseMean+1)), color='gray', linetype='dotted') +
         geom_boxplot(fill=unique(plot_data$Module)) + theme_minimal() + 
         xlab('Modules ordered by Module-Diagnosis correlation') + ylab('log2(Mean Expression)'))

plot_data %>% ggplot(aes(MTcor, log2(baseMean+1))) + geom_point(alpha=0.2, color=plot_data$Module, aes(id=ID)) + 
         geom_hline(yintercept=mean(log2(plot_data$baseMean+1)), color='gray', linetype='dotted') + 
         geom_smooth(color='gray', alpha=0.3) + theme_minimal() + xlab('Module-Diagnosis correlation') +
         ggtitle(paste0('R^2=',round(cor(plot_data$MTcor, log2(plot_data$baseMean+1))^2,4)))

Conclusion:

All of the variables seem to agree with each other, Modules with a high correlation with Diagnosis tend to have genes with high values of Log Fold Change as well as high values of Gene Significance, and the gray module, which groups all the genes that weren’t assigned to any cluster tends to have a very poor performance in all of the metrics.

SFARI Scores

Since SFARI scores genes depending on the strength of the evidence linking it to the development of autism, in theory, there should be some concordance between the metrics we have been studying above and these scores…

SFARI Scores vs Gene Significance

There is not a strong relation between SFARI scores and Gene Significance any more

ggplotly(plot_data %>% ggplot(aes(gene.score, abs(GS), fill=gene.score)) + geom_boxplot() + 
         scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() + 
         ylab('abs(Gene Significance)') + xlab('SFARI Scores') + theme(legend.position='none'))

SFARI Scores vs Module-Diagnosis correlation

The same seems to have happened to the relation between SFARI score and Module-Diagnosis correlation

ggplotly(plot_data %>% ggplot(aes(gene.score, abs(MTcor), fill=gene.score)) + geom_boxplot() + 
         scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() + 
         ylab('abs(Module-Diagnosis Correlation)') + xlab('SFARI Scores') + theme(legend.position='none'))

Conclusion:

This time, the SFARI scores don’t seem to have a strong relation with either Gene Significance or Module-Diagnosis correlation

Session info

sessionInfo()

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] doParallel_1.0.15     iterators_1.0.12      foreach_1.4.7        
##  [4] polycor_0.7-10        expss_0.10.1          WGCNA_1.68           
##  [7] fastcluster_1.1.25    dynamicTreeCut_1.63-1 GGally_1.4.0         
## [10] gridExtra_2.3         viridis_0.5.1         viridisLite_0.3.0    
## [13] RColorBrewer_1.1-2    dendextend_1.13.3     plotly_4.9.2         
## [16] glue_1.3.1            reshape2_1.4.3        forcats_0.4.0        
## [19] stringr_1.4.0         dplyr_0.8.3           purrr_0.3.3          
## [22] readr_1.3.1           tidyr_1.0.2           tibble_2.1.3         
## [25] ggplot2_3.2.1         tidyverse_1.3.0      
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1                backports_1.1.5            
##   [3] Hmisc_4.2-0                 plyr_1.8.5                 
##   [5] lazyeval_0.2.2              splines_3.6.0              
##   [7] BiocParallel_1.20.1         crosstalk_1.0.0            
##   [9] GenomeInfoDb_1.22.0         robust_0.4-18.2            
##  [11] digest_0.6.24               htmltools_0.4.0            
##  [13] GO.db_3.10.0                fansi_0.4.1                
##  [15] magrittr_1.5                checkmate_1.9.4            
##  [17] memoise_1.1.0               fit.models_0.5-14          
##  [19] cluster_2.0.8               annotate_1.64.0            
##  [21] modelr_0.1.5                matrixStats_0.55.0         
##  [23] colorspace_1.4-1            blob_1.2.1                 
##  [25] rvest_0.3.5                 rrcov_1.4-7                
##  [27] haven_2.2.0                 xfun_0.8                   
##  [29] crayon_1.3.4                RCurl_1.95-4.12            
##  [31] jsonlite_1.6                genefilter_1.68.0          
##  [33] impute_1.60.0               survival_2.44-1.1          
##  [35] gtable_0.3.0                zlibbioc_1.32.0            
##  [37] XVector_0.26.0              DelayedArray_0.12.2        
##  [39] BiocGenerics_0.32.0         DEoptimR_1.0-8             
##  [41] scales_1.1.0                mvtnorm_1.0-11             
##  [43] DBI_1.1.0                   Rcpp_1.0.3                 
##  [45] xtable_1.8-4                htmlTable_1.13.1           
##  [47] foreign_0.8-71              bit_1.1-15.2               
##  [49] preprocessCore_1.48.0       Formula_1.2-3              
##  [51] stats4_3.6.0                htmlwidgets_1.5.1          
##  [53] httr_1.4.1                  ellipsis_0.3.0             
##  [55] acepack_1.4.1               pkgconfig_2.0.3            
##  [57] reshape_0.8.8               XML_3.99-0.3               
##  [59] farver_2.0.3                nnet_7.3-12                
##  [61] dbplyr_1.4.2                locfit_1.5-9.1             
##  [63] later_1.0.0                 tidyselect_0.2.5           
##  [65] labeling_0.3                rlang_0.4.4                
##  [67] AnnotationDbi_1.48.0        munsell_0.5.0              
##  [69] cellranger_1.1.0            tools_3.6.0                
##  [71] cli_2.0.1                   generics_0.0.2             
##  [73] RSQLite_2.2.0               broom_0.5.4                
##  [75] fastmap_1.0.1               evaluate_0.14              
##  [77] yaml_2.2.0                  knitr_1.24                 
##  [79] bit64_0.9-7                 fs_1.3.1                   
##  [81] robustbase_0.93-5           nlme_3.1-139               
##  [83] mime_0.9                    xml2_1.2.2                 
##  [85] compiler_3.6.0              rstudioapi_0.10            
##  [87] reprex_0.3.0                geneplotter_1.64.0         
##  [89] pcaPP_1.9-73                stringi_1.4.6              
##  [91] lattice_0.20-38             Matrix_1.2-17              
##  [93] vctrs_0.2.2                 pillar_1.4.3               
##  [95] lifecycle_0.1.0             data.table_1.12.8          
##  [97] bitops_1.0-6                httpuv_1.5.2               
##  [99] GenomicRanges_1.38.0        R6_2.4.1                   
## [101] latticeExtra_0.6-28         promises_1.1.0             
## [103] IRanges_2.20.2              codetools_0.2-16           
## [105] MASS_7.3-51.4               assertthat_0.2.1           
## [107] SummarizedExperiment_1.16.1 DESeq2_1.26.0              
## [109] withr_2.1.2                 S4Vectors_0.24.3           
## [111] GenomeInfoDbData_1.2.2      mgcv_1.8-28                
## [113] hms_0.5.3                   grid_3.6.0                 
## [115] rpart_4.1-15                rmarkdown_1.14             
## [117] Cairo_1.5-10                Biobase_2.46.0             
## [119] shiny_1.4.0                 lubridate_1.7.4            
## [121] base64enc_0.1-3

WGCNA Modules Exploratory Analysis for Frontal Lobe

Dynamic Tree vs Dyamic Hybrid

Conclusion:

Dynamic Hybrid Modules

Relation to external clinical traits

Quantifying module-trait associations

Gene Significance and Module Membership

Analysing concordance between these metrics in the genes

1. Gene Significance vs Log Fold Change

2. Module-Diagnosis correlation vs Gene Significance

3. Module-Diagnosis correlation vs Log Fold Change

4. Module-Diagnosis vs Mean Expression

Conclusion:

SFARI Scores

SFARI Scores vs Gene Significance

SFARI Scores vs Module-Diagnosis correlation

Conclusion:

Session info